






"""simple docstring"""
import argparse
import os
import re


snake_case : Dict     		=		"""src/transformers"""

# Pattern that looks at the indentation in a line.
snake_case : Dict     		=		re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
snake_case : Optional[int]     		=		re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
snake_case : Tuple     		=		re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
snake_case : Dict     		=		re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
snake_case : int     		=		re.compile(r"""\[([^\]]+)\]""")


def 							A						(				__snake_case: Optional[int]  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    _re_indent.search(__snake_case  )
       return "" if search is None else search.groups()[0]


def 							A						(				__snake_case: List[str] ,     __snake_case: Union[str, Any]="" ,     __snake_case: str=None ,     __snake_case: Dict=None  )    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    code.split('\n'  )
       if start_prompt is not None:
              while not lines[index].startswith(__snake_case  ):
                     index += 1
              __magic_name__      				=    ['\n'.join(lines[:index]  )]
       else:
              __magic_name__      				=    []

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       __magic_name__      				=    [lines[index]]
       index += 1
       while index < len(__snake_case  ) and (end_prompt is None or not lines[index].startswith(__snake_case  )):
              if len(lines[index]  ) > 0 and get_indent(lines[index]  ) == indent_level:
                     if len(__snake_case  ) > 0 and get_indent(current_block[-1]  ).startswith(indent_level + ' '  ):
                            current_block.append(lines[index]  )
                            blocks.append('\n'.join(__snake_case  )  )
                            if index < len(__snake_case  ) - 1:
                                   __magic_name__      				=    [lines[index + 1]]
                                   index += 1
                            else:
                                   __magic_name__      				=    []
                     else:
                            blocks.append('\n'.join(__snake_case  )  )
                            __magic_name__      				=    [lines[index]]
              else:
                     current_block.append(lines[index]  )
              index += 1

       # Adds current block if it's nonempty.
       if len(__snake_case  ) > 0:
              blocks.append('\n'.join(__snake_case  )  )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(__snake_case  ):
              blocks.append('\n'.join(lines[index:]  )  )

       return blocks


def 							A						(				__snake_case: List[str]  )    ->		Dict:

       """simple docstring"""





       def _inner(__snake_case: Any  ):
              return key(__snake_case  ).lower().replace('_' ,     ''  )

       return _inner


def 							A						(				__snake_case: List[str] ,     __snake_case: int=None  )    ->		str:

       """simple docstring"""





       def noop(__snake_case: List[Any]  ):
              return x

       if key is None:
              __magic_name__      				=    noop
       # Constants are all uppercase, they go first.
       __magic_name__      				=    [obj for obj in objects if key(__snake_case  ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       __magic_name__      				=    [obj for obj in objects if key(__snake_case  )[0].isupper() and not key(__snake_case  ).isupper()]
       # Functions begin with a lowercase, they go last.
       __magic_name__      				=    [obj for obj in objects if not key(__snake_case  )[0].isupper()]

       __magic_name__      				=    ignore_underscore(__snake_case  )
       return sorted(__snake_case ,     key=__snake_case  ) + sorted(__snake_case ,     key=__snake_case  ) + sorted(__snake_case ,     key=__snake_case  )


def 							A						(				__snake_case: Any  )    ->		Dict:

       """simple docstring"""





       def _replace(__snake_case: Any  ):
              __magic_name__      				=    match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              __magic_name__      				=    [part.strip().replace('"' ,     ''  ) for part in imports.split(','  )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]  ) == 0:
                     __magic_name__      				=    keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__snake_case  )]  ) + "]"

       __magic_name__      				=    import_statement.split('\n'  )
       if len(__snake_case  ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              __magic_name__      				=    2 if lines[1].strip() == '[' else 1
              __magic_name__      				=    [(i, _re_strip_line.search(__snake_case  ).groups()[0]) for i, line in enumerate(lines[idx:-idx]  )]
              __magic_name__      				=    sort_objects(__snake_case ,     key=lambda __snake_case  : x[1]  )
              __magic_name__      				=    [lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]  )
       elif len(__snake_case  ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]  ) is not None:
                     __magic_name__      				=    _re_bracket_content.sub(_replace ,     lines[1]  )
              else:
                     __magic_name__      				=    [part.strip().replace('"' ,     ''  ) for part in lines[1].split(','  )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]  ) == 0:
                            __magic_name__      				=    keys[:-1]
                     __magic_name__      				=    get_indent(lines[1]  ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(__snake_case  )]  )
              return "\n".join(__snake_case  )
       else:
              # Finally we have to deal with imports fitting on one line
              __magic_name__      				=    _re_bracket_content.sub(_replace ,     __snake_case  )
              return import_statement


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: List[str]=True  )    ->		Optional[int]:

       """simple docstring"""





       with open(__snake_case ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       __magic_name__      				=    split_code_in_indented_blocks(
           __snake_case ,     start_prompt='_import_structure = {' ,     end_prompt='if TYPE_CHECKING:'  )

       # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1 ,     len(__snake_case  ) - 1  ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              __magic_name__      				=    main_blocks[block_idx]
              __magic_name__      				=    block.split('\n'  )

              # Get to the start of the imports.
              __magic_name__      				=    0
              while line_idx < len(__snake_case  ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            __magic_name__      				=    len(__snake_case  )
                     else:
                            line_idx += 1
              if line_idx >= len(__snake_case  ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              __magic_name__      				=    '\n'.join(block_lines[line_idx:-1]  )
              __magic_name__      				=    get_indent(block_lines[1]  )
              # Slit the internal block into blocks of indent level 1.
              __magic_name__      				=    split_code_in_indented_blocks(__snake_case ,     indent_level=__snake_case  )
              # We have two categories of import key: list or _import_structure[key].append/extend
              __magic_name__      				=    _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              __magic_name__      				=    [(pattern.search(__snake_case  ).groups()[0] if pattern.search(__snake_case  ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              __magic_name__      				=    [(i, key) for i, key in enumerate(__snake_case  ) if key is not None]
              __magic_name__      				=    [x[0] for x in sorted(__snake_case ,     key=lambda __snake_case  : x[1]  )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              __magic_name__      				=    0
              __magic_name__      				=    []
              for i in range(len(__snake_case  )  ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]  )
                     else:
                            __magic_name__      				=    sort_objects_in_import(internal_blocks[sorted_indices[count]]  )
                            reorderded_blocks.append(__snake_case  )
                            count += 1

        # And we put our main block back together with its first and last line.
              __magic_name__      				=    '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]  )

       if code != "\n".join(__snake_case  ):
              if check_only:
                     return True
              else:
                     print(F"""Overwriting {file}."""  )
                     with open(__snake_case ,     'w' ,     encoding='utf-8'  ) as f:
                            f.write('\n'.join(__snake_case  )  )


def 							A						(				__snake_case: Dict=True  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    []
       for root, _, files in os.walk(__snake_case  ):
              if "__init__.py" in files:
                     __magic_name__      				=    sort_imports(os.path.join(__snake_case ,     '__init__.py'  ) ,     check_only=__snake_case  )
                     if result:
                            __magic_name__      				=    [os.path.join(__snake_case ,     '__init__.py'  )]
       if len(__snake_case  ) > 0:
              raise ValueError(F"""Would overwrite {len(__snake_case  )} files, run `make style`."""  )


if __name__ == "__main__":
 snake_case : Any     		=		argparse.ArgumentParser()
 parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
 snake_case : Optional[Any]     		=		parser.parse_args()

 sort_imports_in_all_inits(check_only=args.check_only)







"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case : Any     		=		logging.getLogger(__name__)
snake_case : Optional[Any]     		=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case : Optional[Any]     		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a_)}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}							,	)
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                  raise ValueError(
                      '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """The input training data file (a text file)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
    __UpperCAmelCase           = field(
        default=5							,	metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The number of processes to use for the preprocessing."""}							,	)
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }							,	)







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.train_file is not None:
                  __magic_name__      				=    self.train_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
           if self.validation_file is not None:
                  __magic_name__      				=    self.validation_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							A						(				__snake_case: Tuple ,     __snake_case: str  )    ->		Tuple:

       """simple docstring"""





       with open(__snake_case ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.loads(__snake_case  ) for line in f.read().splitlines() if (len(__snake_case  ) > 0 and not line.isspace())]
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    {c: dataset[c] for c in dataset.column_names}
       __magic_name__      				=    refs
       return Dataset.from_dict(__snake_case  )


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank  ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s' ,     __snake_case  )

       # Set seed before initializing model.
       set_seed(training_args.seed  )

       # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
       # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
       # (the dataset will be downloaded automatically from the datasets Hub).
       #
       # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
       # 'text' is found. You can easily tweak this behavior (see below).
       #
       # In distributed training, the load_dataset function guarantee that only one local process can concurrently
       # download the dataset.
       if data_args.dataset_name is not None:
              # Downloading and loading a dataset from the hub.
              __magic_name__      				=    load_dataset(data_args.dataset_name ,     data_args.dataset_config_name  )
              if "validation" not in datasets.keys():
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[:{data_args.validation_split_percentage}%]""" ,     )
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[{data_args.validation_split_percentage}%:]""" ,     )
       else:
              __magic_name__      				=    {}
              if data_args.train_file is not None:
                     __magic_name__      				=    data_args.train_file
              if data_args.validation_file is not None:
                     __magic_name__      				=    data_args.validation_file
              __magic_name__      				=    data_args.train_file.split('.'  )[-1]
              if extension == "txt":
                     __magic_name__      				=    'text'
              __magic_name__      				=    load_dataset(__snake_case ,     data_files=__snake_case  )
       # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
       # https://huggingface.co/docs/datasets/loading_datasets.html.

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.config_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    CONFIG_MAPPING[model_args.model_type]()
              logger.warning('You are instantiating a new config instance from scratch.'  )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""  )
                     config.update_from_string(model_args.config_overrides  )
                     logger.info(F"""New config: {config}"""  )

       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'use_fast': model_args.use_fast_tokenizer,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.tokenizer_name:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.tokenizer_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              raise ValueError(
                  'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
                  'You can do it from another script, save it, and load it from here, using --tokenizer_name.'  )

       if model_args.model_name_or_path:
              __magic_name__      				=    AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              logger.info('Training new model from scratch'  )
              __magic_name__      				=    AutoModelForMaskedLM.from_config(__snake_case  )

       model.resize_token_embeddings(len(__snake_case  )  )

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              __magic_name__      				=    datasets['train'].column_names
       else:
              __magic_name__      				=    datasets['validation'].column_names
       __magic_name__      				=    'text' if 'text' in column_names else column_names[0]

       __magic_name__      				=    'max_length' if data_args.pad_to_max_length else False

       def tokenize_function(__snake_case: str  ):
              # Remove empty lines
              __magic_name__      				=    [line for line in examples['text'] if len(__snake_case  ) > 0 and not line.isspace()]
              return tokenizer(examples['text'] ,     padding=__snake_case ,     truncation=__snake_case ,     max_length=data_args.max_seq_length  )

       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     num_proc=data_args.preprocessing_num_workers ,     remove_columns=[text_column_name] ,     load_from_cache_file=not data_args.overwrite_cache ,     )

       # Add the chinese references if provided
       if data_args.train_ref_file is not None:
              __magic_name__      				=    add_chinese_references(tokenized_datasets['train'] ,     data_args.train_ref_file  )
       if data_args.validation_ref_file is not None:
              __magic_name__      				=    add_chinese_references(
                  tokenized_datasets['validation'] ,     data_args.validation_ref_file  )
       # If we have ref files, need to avoid it removed by trainer
       __magic_name__      				=    data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              __magic_name__      				=    False

       # Data collator
       # This one will take care of randomly masking the tokens.
       __magic_name__      				=    DataCollatorForWholeWordMask(tokenizer=__snake_case ,     mlm_probability=data_args.mlm_probability  )

       # Initialize our Trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None ,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
                     __magic_name__      				=    model_args.model_name_or_path
              else:
                     __magic_name__      				=    None
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'train_results.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Train results *****'  )
                            for key, value in sorted(train_result.metrics.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir ,     'trainer_state.json'  )  )

    # Evaluation
       __magic_name__      				=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'  )

              __magic_name__      				=    trainer.evaluate()

              __magic_name__      				=    math.exp(eval_output['eval_loss']  )
              __magic_name__      				=    perplexity

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'eval_results_mlm_wwm.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Eval results *****'  )
                            for key, value in sorted(results.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

       return results


def 							A						(				__snake_case: List[str]  )    ->		Union[str, Any]:

       """simple docstring"""





       main()


if __name__ == "__main__":
 main()